# Nick Maciel

Nick Maciel creates practical LinkedIn content about Google Ads and AI-assisted ad creative, including Performance Max and Demand Gen workflows. Their posts also describe Cadversity's work in product development and manufacturing.

- Profile: https://b2binfluencer.marketing/creators/nick-maciel/
- LinkedIn: https://www.linkedin.com/in/nickdmaciel
- LinkedIn followers: 2,305
- Topics: artificial-intelligence, paid-ads, b2b-marketing, creative-testing, google-ads, agency-operations, performance-max
- Based on 14 dated posts, latest 2026-09-01

## Summary

Useful for practical Google Ads and AI creative workflows, especially Performance Max and Demand Gen asset testing.

- Best for: Ecommerce agency or DTC brand with live Performance Max and Demand Gen campaigns.
- Not for: Consumer brand seeking high-reach sponsored creator campaign with rate card and usage rights.
- Check first: Confirm outcome data, per-post results, and follower demographics before contacting them.

### Takeaways

- Prepare the account before the feature arrives, because inputs decide output.
- Use three to five base photos and generate ten to twenty backgrounds.
- Check off-brand colours, distorted hands or faces, and false product claims.

## Main ideas

- **AI tools are table stakes; the edge is the system built around them**: The recurring claim is that adoption no longer sets anyone apart: the same generative features are available to every advertiser and agency. The difference lies in defining the rules and inputs the tools work from—and assigning someone to own that process. Most companies use AI, the posts argue, but far fewer scale its value beyond adoption. The practical conclusion: the job shifts from producing the work to defining the system that produces it.
- **Prepare the account before the feature arrives, because inputs decide output**: The priority is to get the raw material in order before a rollout, rather than react once it lands. AI assembles what it is fed, the posts argue, so poor inputs produce poor output at greater volume. Feed hygiene, naming and asset standards determine whether a new creative feature helps or adds clutter. Timing matters, too: accounts that are ready move first.
- **Competing on creative volume means structuring assets in reusable blocks**: Most accounts lack asset variety, the posts argue; production cost, not ideas, was the constraint. When images and copy can be recombined automatically, the limit becomes how well those inputs are organised. Nick Maciel recommends thinking in blocks rather than finished ads, so the same images and messages can be mixed into audience-specific combinations without a new shoot or edit.
- **Let measured performance, not opinion, choose the creative**: The posts pair the push for volume with test design and grading: run a control-and-variant experiment, define the metrics, keep what wins and pause the rest. They also call for guardrails. Generated creative needs review for brand fit, visual errors and claims, and a person remains accountable for what goes live.
- **Brand consistency and performance are no longer a trade-off**: The sharpest idea in the brand-consistency posts is that the old complaint—Performance Max performed well but looked nothing like the advertiser—is now, in Nick Maciel's account, a configuration problem rather than a permanent cost of automation. Define fonts, colours and voice once, and the system has a spine to generate against; testing aggressively and staying on brand no longer pull in opposite directions. Someone still has to own the profile and review output on a schedule.
- **A business audit is how you find the growth lever a working company is missing**: A separate strand, written in the voice of Cadversity's CEO, argues that even successful businesses leave market reach on the table and that regular strategy reviews can uncover the opportunity. The claim is less about selling more than finding parts of the route to market a business has never built. A companion thread frames manufacturing and product-development mistakes as learning opportunities rather than failures—the same review-and-correct instinct applied to operations.

## Work to read

- [Google Demand Gen turns static assets into video ads](https://www.linkedin.com/posts/nickdmaciel_google-is-about-to-flip-video-ads-on-their-activity-7403585822802989056-hVLX)
- [Using Performance Max AI image tools to cut design cost and scale creative](https://www.linkedin.com/posts/nickdmaciel_google-just-handed-pmax-advertisers-a-cheat-activity-7411451282927820800-WDKI)
- [Brand Profiles give Performance Max a brand spine for AI assets](https://www.linkedin.com/posts/nickdmaciel_google-ads-quietly-fixed-the-biggest-problem-activity-7408552174164594688-dafH)
- [The reporting hours an agency can reclaim through automation](https://www.linkedin.com/posts/nickdmaciel_agencylife-marketingagency-aiautomation-activity-7500537937974022144-mEfP)
- [A business audit at the Nuts Factory and the missing distribution arm](https://www.linkedin.com/posts/nickdmaciel_leadershipinsights-businessgrowth-cadversityexperience-activity-7154232137062256640-lGMK)

## Brand relationships

- **Cadversity** (employment)
- **Nuts Factory** (client)

## Partnerships

The posts contain no sponsorship policy, rate card, media kit, or partnership inquiry route. They offer teaching-style commentary rather than branded placements: numbered, step-by-step LinkedIn breakdowns of Google Ads tooling — generative AI video inside Demand Gen, AI image expansion and background generation in Performance Max, and Brand Profiles for on-brand asset generation — alongside posts about Cadversity's product-development and manufacturing audit work. For a buyer planning an engagement, the public material reads as a work sample, not an offer sheet. The Google Ads posts show the work they describe: auditing an account, cleaning product feeds, standardising image sizes and naming structures, structuring creative into reusable blocks, and running control-versus-variant experiments inside Performance Max before rolling out. Brands can evaluate those operational requirements before contacting them. Ask directly about deliverables, usage rights, disclosure, exclusivity, review windows, and pricing. The LinkedIn account associated with the posts listed roughly 2,300 followers, a figure to consider when sizing a paid test.

## Alternatives

- Mike Futia: Choose Mike Futia when the bottleneck is creative volume and production cost, not account structure. The posts offer practical playbooks for turning one product photo into dozens of campaign-ready images: uploading up to 14 reference images to keep logos, labels and packaging accurate; generating lifestyle scenes from a single SKU; swapping models to test different buyer personas; rendering readable text inside the image in any language; relighting a winning ad to fight fatigue; and adapting images to different aspect ratios without cropping the product. Nick Maciel's posts cover AI creative inside Performance Max and Demand Gen, but focus on directing the account. Futia's material gets into the generation workflow, with named protocols (Anchor Stack, Mannequin Swap, Hook Factory, Science Ad) and a sample output. For an ecommerce client whose Performance Max ceiling is asset supply rather than bidding, Futia is the more specific choice.
- Martin Davies: Choose Martin Davies when the task is diagnosing a live Google Ads account rather than producing new creative. The posts answer two audit questions that Nick Maciel's posts leave open. First, how to tell whether a sales dip comes from lower market demand or from competitor activity: run an impression share report by day for the past 30 days or from before the dip, and if share holds steady the problem is demand, while a falling share points to competitive pressure or a competitor promotion. Second, whether brand traffic should be excluded from Performance Max, judged with a script that separates branded and non-branded clicks, conversions and share of brand revenue over time, and a recommendation to run brand through search campaigns instead if brand is taking a high share of Performance Max traffic. If the agency need is to understand why an account slowed down, or whether it is paying for conversions it would have received anyway, this information addresses the question directly.
- Robin Tesselaar: Choose Robin Tesselaar when the question is whether an account is genuinely profitable and how to structure Performance Max to keep it that way. The posts argue that ROAS is not return: it is revenue divided by ad spend, before cost of goods, shipping, returns and fulfilment. They give the arithmetic: at 35% margin, break-even ROAS is 2.86x, so anything below that loses money. Tesselaar recommends measuring profit on ad spend and new-customer acquisition cost. The posts also warn that in brand-dominant accounts, 60 to 80% of attributed conversions may have happened without the ad, and that average ROAS hides marginal returns. Worked numbers show a lower headline-ROAS channel generating more profit on incremental spend. The recommended Performance Max structure is specific: protect branded traffic with a dedicated exact match search campaign so Performance Max competes only for cold traffic; split asset groups by margin with separate target ROAS settings; feed CRM and lifetime-value audiences; use conversion value rules so a $20 sale and a $200 sale aren't treated as the same signal; and use search themes only for discovery terms not already captured.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/